CN115820857A - Kit for identifying precancerous lesions of stomach cancer and diagnosing stomach cancer - Google Patents
Kit for identifying precancerous lesions of stomach cancer and diagnosing stomach cancer Download PDFInfo
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Abstract
The invention discloses a kit for identifying precancerous lesions of gastric cancer and diagnosing gastric cancer. The invention provides a kit, a comprehensive diagnosis model and a comprehensive diagnosis system for distinguishing gastric precancerous lesion and gastric cancer and diagnosing gastric cancer based on a marker combination consisting of RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880. By detecting the expression quantity of the marker combination, the comprehensive diagnosis model can be used for distinguishing gastric cancer (including early gastric cancer) patients from healthy people and gastric cancer from precancerous lesion patients, and is favorable for early discovery and correct treatment of gastric cancer.
Description
Technical Field
The invention belongs to the technical field of biological medicines. More particularly, it relates to a kit for identifying precancerous lesions of gastric cancer and diagnosing gastric cancer.
Background
With the enhancement of public health consciousness and the improvement of social medical conditions, the incidence rate of gastric cancer is generally reduced in recent years, but the mortality rate is still high, and one reason of the reduction is that the early screening of gastric cancer is not strong or does not want to be screened by invasive examination means such as gastroscopy. In addition, gastric cancer is a multi-step canceration process, and the progression from normal gastric mucosal epithelium to gastric cancer is characterized by chronic atrophic gastritis, intestinal metaplasia, gastric ulcer, gastric polyp, etc., which are all the categories of pre-gastric precancerous lesions. However, the existing markers for identifying the precancerous lesions of the stomach and the stomach cancer are few, a method for non-invasively identifying the precancerous lesions of the stomach and the stomach cancer is lacked, and the early symptoms of the precancerous lesions of the stomach and the early symptoms of the stomach cancer are not obviously different, so that most patients do not pay attention to the early symptoms, correct treatment is not carried out aiming at the early stage of the stomach cancer, and the treatment effect of the patients with the advanced stomach cancer is poor when diagnosis is confirmed. Therefore, the marker which can identify gastric precancerous lesions and gastric cancer and can carry out early diagnosis of gastric cancer has important value, and is beneficial to early screening of gastric cancer and correct treatment of patients.
Fluid biopsy is a non-invasive means of detection, and allows early detection and therapeutic stratification of cancer by circulating tumor cells, circulating free tumor DNA, circulating free tumor RNA, and extracellular vesicles in blood and other body fluids. Exosomes are vesicles released by cells (including cancer cells) into the surrounding biological fluid, which have the advantage of high copy number and are easier to detect; at the same time, exosomes are very stable in biological fluids such as plasma and urine, and can be isolated for clinical evaluation even in the early stages of the disease. Therefore, the exosome-based biomarker has been rapidly applied to the clinical field, and cancer diagnosis can be performed by detecting tumor-derived substances, such as DNA, RNA (including long non-coding RNA (lncRNA) and circular RNA (circRNA)), protein and lipid, contained in exosome.
There are both long non-coding RNA and circular RNA in the presently disclosed gastric cancer biomarkers based on exosomes, but there are few reports of determining gastric cancer by combining both. And most gastric cancer biomarkers can only be used for diagnosing whether a subject has gastric cancer, and the research on how to distinguish early gastric cancer from normal people, gastric cancer and precancerous lesion is not deep enough, so that the method is not beneficial to the correct treatment of patients.
Disclosure of Invention
The invention aims to solve the technical problem of how to enrich the biomarkers for gastric precancerous lesions and early diagnosis of gastric cancer, and provides a kit for diagnosing gastric cancer or identifying gastric cancer and gastric precancerous lesions.
It is a first object of the present invention to provide a marker combination for use in the identification of gastric precancerous lesions and gastric cancer or for use in the diagnosis of gastric cancer.
The second purpose of the invention is to provide the application of the reagent for detecting the expression quantity of the marker combination in the preparation of products for gastric cancer diagnosis.
The third purpose of the invention is to provide the application of the reagent for detecting the expression quantity of the marker combination in the preparation of products for identifying gastric cancer and precancerous lesion of stomach.
The fourth purpose of the invention is to provide a kit for identifying gastric precancerous lesions and gastric cancer and diagnosing gastric cancer.
The fifth purpose of the invention is to provide a comprehensive diagnosis model for identifying gastric precancerous lesions and gastric cancer and diagnosing gastric cancer.
It is a sixth object of the present invention to provide a comprehensive diagnostic system for discriminating between gastric precancerous lesions and gastric cancer and diagnosing gastric cancer.
The above purpose of the invention is realized by the following technical scheme:
the invention provides a marker combination for identifying gastric precancerous lesions and gastric cancer or for gastric cancer diagnosis, which comprises 7 lncRNA and 1 circRNA.
Specifically, the marker combination consists of RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880, and the cDNA sequences of the marker combination are shown as SEQ ID NO. 1-SEQ ID NO.8 in sequence; wherein, RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-2339L15.3, DGCR9 and LINC00567 are lncRNA, and hsa _ circ _0047880 is circRNA.
The invention detects the serum exosomes of gastric cancer patients and healthy human, and the marker combination in the gastric cancer tissues and the cancer-adjacent tissues respectively, namely detects the expression level of RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 in the samples and compares the expression levels to find that the expression level of the markers in the serum exosomes of the gastric cancer patients is obviously higher than that of the healthy people, and the expression level in the gastric cancer tissues is also obviously higher than that of the cancer-adjacent tissues.
Meanwhile, the invention establishes a comprehensive diagnosis model by using the marker combination, namely RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 as target markers, analyzes the diagnosis efficiency of the model to find that gastric cancer can be diagnosed by using the model, and distinguishes gastric cancer patients from healthy people; even if the subject is in the early stage of gastric cancer, the subject can be distinguished from healthy people, and the diagnosis of gastric cancer and the early large-scale screening of gastric cancer can be performed by detecting the expression amount of the marker combination. In addition to its use in diagnosing gastric cancer, the present invention also finds use in the comprehensive diagnostic model to identify gastric and gastric precancerous lesions.
Therefore, the invention claims the application of the reagent for detecting the expression level of the marker combination in preparing products for gastric cancer diagnosis.
The invention also claims application of the reagent for detecting the expression quantity of the marker combination in preparing products for identifying gastric cancer and gastric precancerous lesion.
Specifically, the marker combinations were RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880.
On the basis of the marker combination, the invention also provides a kit for identifying gastric precancerous lesions and gastric cancer and diagnosing gastric cancer, and the kit contains a reagent for detecting the expression quantity of the marker combination.
On the basis of the sequences of the marker combinations disclosed by the invention, the marker combinations can be quantitatively detected by methods such as fluorescent quantitative PCR and the like.
Specifically, the kit comprises a fluorescent quantitative PCR primer for detecting the expression quantity of the marker combination.
As an alternative embodiment, the sequence of the fluorescent quantitative PCR primer for detecting RP11-556O9.4 is shown as SEQ ID NO. 9-10; the sequence of the fluorescent quantitative PCR primer for detecting RP11-417E7.1 is shown in SEQ ID NO. 11-12; the sequence of the fluorescent quantitative PCR primer for detecting RPS3AP23 is shown in SEQ ID NO. 13-14; the sequence of the fluorescent quantitative PCR primer for detecting RP11-559M23.1 is shown as SEQ ID NO. 15-16; the sequence of the fluorescent quantitative PCR primer for detecting CTD-2339L15.3 is shown as SEQ ID NO. 17-18; the sequence of the fluorescent quantitative PCR primer for detecting DGCR9 is shown as SEQ ID NO. 19-20; the sequence of the fluorescent quantitative PCR primer for detecting the LINC00567 is shown in SEQ ID NO. 21-22; the sequence of the fluorescent quantitative PCR primer for detecting hsa _ circ _0047880 is shown in SEQ ID NO. 23-24.
The invention also provides a comprehensive diagnosis model for identifying gastric precancerous lesions and gastric cancer and diagnosing gastric cancer, wherein the model takes a marker combination consisting of RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 as target markers.
Specifically, the model of the present invention calculates a combined diagnostic score (cd-score) using the following equation:
cd-score = -2.31858+0.11316 ha _circ _47880expression amount +3.15386 dgcr9 expression amount +1.48516 linc00567 expression amount +5.13114 ctd-233l15.3 expression amount-1.25785 rp11.417e7.1 expression amount-0.47492 rp11.556o9.4 expression amount +1.22980 rp11.5523.1 expression amount-0.09542 rps3ap23 expression amount.
Specifically, when the model is used for diagnosing gastric cancer and distinguishing gastric cancer patients from healthy people, 0.331 is used as a cutoff value, and if the calculated comprehensive diagnosis index is higher than the cutoff value, the diagnosis result is positive, namely the gastric cancer; if the obtained comprehensive diagnosis index is lower than the cutoff value, the diagnosis result is negative, and the person is a healthy person;
if the subject has already developed gastric symptoms, but cannot distinguish whether the subject is gastric cancer or precancerous lesions only from the symptoms, the comprehensive diagnosis model of the present invention can be used for judgment. When the subject has stomach symptoms and gastric cancer and precancerous lesion are identified by the model, the cutoff value is 0.732, and if the comprehensive diagnosis index obtained by calculation is higher than the cutoff value, the diagnosis result is gastric cancer; if the obtained comprehensive diagnosis index is lower than the cutoff value, the diagnosis result is gastric precancerous lesion.
The invention also provides a comprehensive diagnosis system for identifying gastric precancerous lesions and gastric cancer and diagnosing gastric cancer, which comprises the following modules:
(1) A module for quantitatively detecting the expression amounts of RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 in a sample respectively;
(2) A comprehensive diagnostic index calculation module: cd-score = -2.31858+0.11316 ha _circ _47880expression amount +3.15386 dgcr9 expression amount +1.48516 linc00567 expression amount +5.13114 ctd-233l15.3 expression amount-1.25785 rp11.417e7.1 expression amount-0.47492 rp11.556o9.4 expression amount +1.22980 rp11.5523.1 expression amount-0.09542 rps3ap23 expression amount;
(3) And a result judgment module: when the diagnostic kit is used for diagnosing the gastric cancer, 0.331 is used as a cutoff value, and if the calculated comprehensive diagnostic index is higher than the cutoff value, the diagnostic result is positive, namely the gastric cancer; if the obtained comprehensive diagnosis index is lower than the cutoff value, the diagnosis result is negative, and the person is a healthy person;
when the kit is used for identifying gastric cancer and precancerous lesion, 0.732 is used as a cutoff value, and if the calculated comprehensive diagnosis index is higher than the cutoff value, the diagnosis result is gastric cancer; if the obtained comprehensive diagnosis index is lower than the cutoff value, the diagnosis result is gastric precancerous lesion.
The invention has the following beneficial effects:
the invention provides a kit, a comprehensive diagnosis model and a comprehensive diagnosis system for identifying gastric precancerous lesions and gastric cancer and diagnosing gastric cancer based on a marker combination consisting of 7 lncRNAs and 1 circRNA, namely, based on a marker combination consisting of RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-9233L15.3, DGCR9, LINC00567 and hsa _ circ _0047880. By detecting the expression level of the marker combination, the comprehensive diagnosis model can be used for distinguishing gastric cancer (including early gastric cancer) patients from healthy people and gastric cancer from gastric precancerous lesion patients. The invention provides a noninvasive kit and a noninvasive method for identifying gastric precancerous lesions and gastric cancer and diagnosing gastric cancer, and has important significance for early discovery and correct treatment of gastric cancer.
Drawings
FIG. 1 is a graph of the ROC curves in each set and the optimal cut-off values in the training set using the comprehensive diagnostic model constructed in example 1; wherein, the Training set is a Training set, the Testing set is a test set, and the External validation is an External validation set; best cut-off is the optimal cut-off value; AUC is the area under the curve.
FIG. 2 shows the expression of lncRNA and circRNA molecules in the marker combinations of the present invention in exosomes from human serum samples from patients with gastric cancer and healthy persons; wherein the expression conditions of RP11-556O9.4, RPS3AP23, RP11-417E7.1, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 are sequentially arranged from top to bottom from left to right; t represents a gastric cancer patient sample, and N represents a healthy human sample.
FIG. 3 shows the expression of lncRNA and circRNA molecules in the marker combinations of the present invention in gastric cancer tissues and paracarcinoma tissues of gastric cancer patients; wherein the expression conditions of RP11-556O9.4, RPS3AP23, RP11-417E7.1, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 are sequentially arranged from top to bottom from left to right; t represents gastric cancer tissue sample, and N represents para-cancer tissue sample.
FIG. 4 is a ROC curve and cut-off values for differentiating between early gastric cancer patients and healthy human samples using the comprehensive diagnostic model constructed in example 1; where AUC is the area under the curve.
FIG. 5 is a ROC curve and cut-off values for differentiating gastric cancer (Tumor) patients from gastric Precancerous Lesion (PL) patient samples using the comprehensive diagnostic model constructed in example 1; where AUC is the area under the curve.
Detailed Description
The invention is further described with reference to the drawings and the following detailed description, which are not intended to limit the invention in any way. Reagents, methods and apparatus used in the present invention are conventional in the art unless otherwise indicated.
Unless otherwise indicated, reagents and materials used in the following examples are commercially available.
Serum samples, gastric cancer tissues and tissues beside cancer (tissues beside cancer means normal tissues more than 5cm away from the cancer incisal margin) of gastric cancer patients used in the examples of the present invention were obtained from the center for tumor prevention and treatment of university of Zhongshan or a sixth hospital affiliated to Zhongshan university, and the patients informed and agreed to use.
Example 1 exosome-based screening of gastric cancer biomarkers and establishment of comprehensive diagnostic model
1. Screening of gastric cancer biomarkers based on exosomes
(1) Serum samples of 37 patients with gastric cancer and 20 healthy persons, and gastric cancer tissues and paracancerous tissues of 20 patients with gastric cancer (samples from the center for tumor prevention and treatment of Zhongshan university) were collected, and exosomes were extracted using a serum exosome extraction kit (cat # C10110-2), followed by TRIzol TM Extracting RNA in serum exosome and tissue by using the reagent respectively;
(2) Detecting the RNA expression of all samples extracted in the step (1) by using an RNA-seq high-depth sequencing method, identifying existing and newly-found lncRNA and circRNA), screening lncRNA and circRNA which are highly expressed in serum exosomes of gastric cancer patients compared with healthy human serum exosomes, and lncRNA and circRNA which are highly expressed in gastric cancer tissues compared with tissues beside cancer, taking the intersection of the lncRNA and the circRNA, and combining the lncRNA which is highly expressed in the gastric cancer tissues in a TCGA database to obtain a candidate lncRNA marker; obtaining a candidate circRNA marker by taking the intersection of the two circRNA molecules;
(3) Designing specific fluorescent quantitative PCR primers of the lncRNA and circRNA molecules obtained in the step (2);
(4) Serum samples of 36 patients with gastric cancer and 36 healthy persons, and gastric cancer tissues and tissues beside the cancer (samples) of 24 patients with gastric cancerFrom center for tumor prevention and treatment, university of Zhongshan) using TRIzol TM Sample RNA was extracted with a reagent, and PrimeScript was used TM The RT reagent Kit reverse transcription Kit is used for reverse transcribing RNA into cDNA (the product number is RR037A; the Kit is suitable for lncRNA and circRNA; external reference (External Standard Kit (lambda polyA) for qPCR, the product number is 3789) is additionally added when serum exosome RNA is subjected to reverse transcription, 500ng of RNA and 0.1ng of lambda polyA are added in a 10 mu L reverse transcription system, cDNA of serum exosomes and tissues is respectively obtained, and the reverse transcription process is carried out according to the Kit instruction; verifying lncRNA and circRNA screened in the step (2) by using the specific primers designed in the step (3) and a fluorescent quantitative PCR method, and further screening lncRNA and circRNA molecules with high consistency of gastric cancer serum exosomes and gastric cancer tissues;
(5) Performing real-time fluorescence quantitative PCR detection on training samples, verification samples and external verification samples respectively based on lncRNA and circRNA screened in the step (4) to generate data sets of three independent samples, wherein the training samples and the verification samples are 522 gastric cancer patients and 460 healthy people from the same batch of tumor prevention and treatment center of Zhongshan university, and the proportion is 7.
2. Data analysis and model establishment:
(1) And (4) in the training data set, modeling by multi-factor logistic regression, and screening the fluorescent quantitative PCR result obtained in the step (5) by using a Stepwise method. The invention selects 7 lncRNA and 1 circRNA molecules from candidate lncRNA and circRNA molecules; 7 lncRNAs are respectively RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-2339L15.3, DGCR9 and LINC00567;1 circRNA is hsa _ circ _0047880.
The information of RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 is shown in Table 1, and the cDNA sequences are shown in sequence as SEQ ID NO. 1-8. The 7 lncRNA and 1 circRNA molecules described above were subsequently represented by lncRNA and circRNA molecules in the marker combination.
The sequence of the fluorescent quantitative PCR primers for detecting lncRNA and circRNA molecules in the marker combination is shown as SEQ ID NO:9 to 24 (Table 2).
TABLE 1 molecular markers of lncRNA and circRNA for diagnosis of gastric cancer
TABLE 2 fluorescent quantitative PCR primers
Real-time fluorescent quantitative PCR kit Using Promega: (
qPCR Master Mix, cat #: a6001 The lncRNA and circRNA molecules in the marker combination were quantified, and the PCR reaction detection line was 10. Mu.L. Except for different primer sequences, RT-qPCR amplification reaction systems and reaction procedures of lncRNA and circRNA molecules in the quantitative detection marker combination are the same as shown in the following:
reaction system
Reaction procedure:
10min at 95 ℃; repeating the cycle for 50 times at 95 deg.C for 15sec,60 deg.C for 1 min; 30s at 40 ℃; storing at 4 deg.C.
2. The coefficients for lncRNA and circRNA in the marker combinations described above were determined in the training set and their linear equations are shown below:
the comprehensive diagnostic index (cd-score) = -2.31858+0.11316 has _circ _47880expression amount +3.15386 dgcr9 expression amount +1.48516 linc00567 expression amount +5.13114 ctd-2339l15.3 expression amount-1.25785 rp11.417e7.1 expression amount-0.47492 rp11.556o9.4 expression amount +1.22980 rp11.559m23.1 expression amount-0.09s3ap23 expression amount; the combined diagnostic index was used for validation in serum exosome samples provided in validation cohorts and external validation cohorts.
3. Diagnostic efficacy of integrated diagnostic models
The invention constructs a comprehensive diagnosis model by using lncRNA and circRNA in the marker combination through multi-factor logistic regression modeling and applying a Stepwise method, and the linear equation is as follows:
the overall diagnostic index (cd-score) = -2.31858+0.11316 ha _circ _47880expression level +3.15386 dgcr9 expression level +1.48516 linc00567 expression level +5.13114 ctd-2339l15.3 expression level-1.25785 rp11.417e7.1 expression level-0.47492 rp11.556o9.4 expression level +1.22980 rp11.559m23.1 expression level-0.09s3ap23 expression level.
By utilizing the linear equation, the comprehensive diagnostic index (cd-score) of each sample (training sample, verification sample and external verification sample) is calculated, and ROC curves of corresponding sets are drawn according to the sensitivity and specificity changes of the model at different cutoff values. The optimal cutoff values of the comprehensive diagnosis model constructed by the invention in the ROC curves of all sets and in the training set are shown in FIG. 1; the area under the ROC curve of the Training set (Training set) was 0.961, the area under the ROC curve of the test set (Testing set) was 0.976, and the area under the ROC curve of the External verification set (External evaluation) was 0.939. The optimal cutoff (Best cut-off) of the model in the training set is 0.331, samples in the set are divided into positive (diagnosed as gastric cancer patients) and negative (healthy people), the diagnosis sensitivity is 91.9%, and the specificity is 90.0%. Specifically, the diagnosis process is that when the cd-score of the sample is larger than Best cut-off, the gastric cancer is diagnosed; when the cd-score of the sample is smaller than Best cut-off, the sample is diagnosed as a healthy person.
The results show that the comprehensive diagnosis model can effectively diagnose the gastric cancer, namely, the gastric cancer can be diagnosed by detecting the expression levels of RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-233L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 in a serum exosome sample of a subject and analyzing the change of the expression levels, so that the simplicity and the accuracy of the gastric cancer diagnosis are improved, and the comprehensive diagnosis model has great significance for further diagnosis and treatment of the subject.
Example 2 comparison of expression levels of lncRNA and circRNA in marker combinations in gastric cancer and healthy human serum exosomes
1. Experimental methods
36 serum samples of gastric cancer patients and 36 samples of healthy human serum (from the center for tumor prevention and treatment of Zhongshan university) were selected, RNA of the exosomes of the gastric cancer patients and the healthy human serum was extracted, and then reverse-transcribed into cDNA, and the expression levels of lncRNA and circRNA in the marker combination were detected by RT-qPCR. The primers, reaction system and reaction procedure for exosome extraction, RNA extraction, reverse transcription and RT-qPCR detection were the same as in example 1.
2. Results of the experiment
The expression conditions of lncRNA and circRNA molecules in the marker combination in the exosomes of the human serum samples of the gastric cancer patients and the healthy patients are shown in figure 2; in FIG. 2, the expression profiles of RP11-556O9.4, RPS3AP23, RP11-417E7.1, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 are shown from top to bottom and in sequence from left to right, wherein T represents a gastric cancer patient sample and N represents a healthy human sample. As can be seen from FIG. 2, RP11-556O9.4, RPS3AP23, RP11-417E7.1, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 were expressed in higher amounts in the serum exosomes of gastric cancer patients than in healthy people, and the differences were all significant.
Example 3 comparison of expression levels of IncRNA and circRNA in marker combinations in gastric cancer and paracancerous tissues
1. Experimental methods
Selecting 24 cases of gastric cancer tissues and tissue samples beside the gastric cancer tissues (from the center for tumor prevention and treatment in Zhongshan university), extracting the RNA of the gastric cancer tissues and the tissue samples beside the gastric cancer, carrying out reverse transcription on the RNA to obtain cDNA, and detecting the expression quantity of lncRNA and circRNA in the marker combination through RT-qPCR. The primers, reaction system and reaction procedure used for tissue RNA extraction, reverse transcription and RT-qPCR detection were the same as in example 1.
2. Results of the experiment
The expression of lncRNA and circRNA molecules in the marker combination of the invention in gastric cancer tissues and paracarcinoma tissues of gastric cancer patients is shown in figure 3; in FIG. 3, the expression profiles of RP11-556O9.4, RPS3AP23, RP11-417E7.1, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 are shown from top to bottom and in sequence from left to right, wherein T represents a gastric cancer tissue sample and N represents a cancer-adjacent tissue sample. As can be seen from FIG. 3, RP11-556O9.4, RPS3AP23, RP11-417E7.1, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 were expressed in higher amounts in gastric cancer tissues than in paracarcinoma tissues, and the differences were all significant.
Example 4 differentiation of patients with early gastric cancer from healthy people
In order to verify whether the early diagnosis of gastric cancer can be carried out by detecting the expression conditions of lncRNA and circRNA molecules in the marker combination to distinguish stage I and II gastric cancer patients from healthy people, 47 cases of stage I gastric cancer samples, 171 cases of stage II gastric cancer samples (stage I and stage II according to pTNM stage determination of gastric cancer AJCC 8 edition) and 460 cases of serum samples of healthy people (all healthy human serum samples in the same batch of example 1) are selected from samples in the same batch of example 1, and the expression levels of lncRNA and circRNA molecules in the marker combination are subjected to RT-qPCR detection; the primers used in the reaction are as in Table 2, and the reaction system and procedure are as in example 1.
After obtaining the expression data, the comprehensive diagnostic model in example 1 was used to calculate the comprehensive diagnostic index of the sample and to plot ROC curves according to the sensitivity and specificity changes of the model at different cut-off values. The ROC curve obtained was plotted as shown in FIG. 4, and it is understood from FIG. 4 that the area under the ROC curve was 0.955, cut-off was 0.331, and the samples were classified as positive (i.e., diagnosis of gastric cancer I and II) and negative (i.e., diagnosis of healthy person), and the sensitivity of the diagnosis was 97.2% and the specificity was 71.7%.
The above results indicate that the use of RP11-556O9.4, RPS3AP23, RP11-417E7.1, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 can distinguish between stage I and II gastric cancer patients and healthy persons, and contribute to the early diagnosis of gastric cancer.
Example 5 identification of Pre-gastric lesions and gastric cancer
To verify whether gastric cancer (Tumor) and Precancerous Lesions (PL) were differentiated by detecting the expression of RP11-556O9.4, RPS3AP23, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880, 144 gastric cancer patients (based on gastric cancer AJCC version 8 pTNM classification, in this example 36 patients with stage I, 22 patients with stage II, 61 patients with stage III and 25 patients with stage IV) and 73 gastric precancerous lesions (in this example gastric precancerous lesions include gastric ulcer, intestinal epithelial metaplasia, gastric polyp, chronic atrophic gastritis and atypical hyperplasia) were collected from the university Tumor control center in Zhongshan and were diagnosed by PCR using a combination of serum samples of RT-like RNA markers expressed in clinical specimens and PCR; the primers used in the reaction are as shown in Table 2, and the reaction system and procedure are as in example 1.
After obtaining the expression data, the comprehensive diagnostic model in example 1 was used to calculate the comprehensive diagnostic index of the sample and plot the ROC curve, and the comprehensive diagnostic model was used to determine the sample. The ROC curve obtained was plotted as shown in FIG. 5, and it is understood from FIG. 5 that the area under the ROC curve was 0.675, and the optimum cut-off (Best cut-off) value of the model in this example was 0.732, at which time the sensitivity of diagnosing a sample with the model was 68.7% and the specificity was 65.8%. Specifically, the diagnosis process is that when the cd-score of the sample is larger than Best cut-off, the gastric cancer is diagnosed; when the cd-score of the sample is smaller than Best cut-off, the sample is diagnosed as gastric precancerous lesion.
The above results indicate that the comprehensive diagnostic model constructed using RP11-556O9.4, RPS3AP23, RP11-417E7.1, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 can distinguish patients with gastric cancer (including stage I and II gastric cancer) and gastric precancerous lesion, and help patients to be treated correctly.
Example 6 kit for diagnosing gastric cancer or for identifying gastric cancer and gastric precancerous lesions
The present invention also provides a kit for diagnosing gastric cancer or for identifying gastric cancer and gastric precancerous lesions, which contains a reagent for detecting the expression amounts of RP11-556O9.4, RPS3AP23, RP11-417E7.1, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880. The reagent is a fluorescent quantitative PCR primer for detecting the expression conditions of RP11-556O9.4, RPS3AP23, RP11-417E7.1, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 and a reagent required by fluorescent quantitative PCR reaction.
Specifically, the sequences of the fluorescent quantitative PCR primers are shown in Table 2 of example 1 (i.e., shown in SEQ ID Nos. 9 to 24), and the reaction system and the reaction procedure are the same as those of example 1.
The present invention also provides a method for diagnosing gastric cancer using the above kit and the comprehensive diagnostic model described in example 1, comprising the steps of:
s1, collecting a serum sample, extracting exosome RNA and performing reverse transcription to obtain cDNA, taking the obtained cDNA as a template, performing qPCR reaction by using fluorescent quantitative PCR primers shown in SEQ ID NO. 9-24, and quantitatively detecting the expression quantity of RP11-556O9.4, RPS3AP23, RP11-417E7.1, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _ 0047880;
s2, calculating a comprehensive diagnosis index, wherein a linear equation is as follows: cd-score = -2.31858+0.11316 ha _circ _47880expression amount +3.15386 dgcr9 expression amount +1.48516 linc00567 expression amount +5.13114 ctd-233l15.3 expression amount-1.25785 rp11.417e7.1 expression amount-0.47492 rp11.556o9.4 expression amount +1.22980 rp11.5523.1 expression amount-0.09542 rps3ap23 expression amount.
When the model is used for diagnosing gastric cancer and distinguishing gastric cancer patients from healthy people, 0.331 is used as a cutoff value, and if the calculated comprehensive diagnosis index is higher than the cutoff value, the diagnosis result is positive, namely the gastric cancer is obtained; if the obtained comprehensive diagnosis index is lower than the cutoff value, the diagnosis result is negative, and the person is a healthy person;
if the subject has developed gastric symptoms, but cannot distinguish whether the subject is a gastric cancer or a precancerous lesion from the symptom expression, the comprehensive diagnosis model of the present invention can be used to determine the gastric symptoms. When the subject has developed gastric symptoms and gastric precancerous lesions are identified using the model, 0.732 is used as the cutoff value, and if the calculated composite diagnostic index is higher than the cutoff value, the diagnosis is gastric cancer; if the obtained comprehensive diagnosis index is lower than the cutoff value, the diagnosis result is gastric precancerous lesion.
Based on the kit and the method, the invention also provides a comprehensive diagnosis system for diagnosing gastric cancer or identifying gastric cancer and precancerous lesion, which comprises the following modules:
(1) A module for quantitatively detecting the expression amounts of RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 in a sample respectively;
(2) A comprehensive diagnostic index calculation module: the comprehensive diagnosis index = -2.31858+0.11316 ha _circ _47880expression amount +3.15386 dgcr9 expression amount +1.48516 linc00567 expression amount +5.13114 ctd-2339l15.3 expression amount-1.25785 rp11.417e7.1 expression amount-0.47492 rp11.556o9.4 expression amount +1.22980 rp11.559m23.1 expression amount-0.09542 rps3ap23 expression amount;
(3) And a result judgment module: when the diagnostic kit is used for diagnosing the gastric cancer, 0.331 is used as a cutoff value, and if the calculated comprehensive diagnostic index is higher than the cutoff value, the diagnostic result is positive, namely the gastric cancer; if the obtained comprehensive diagnosis index is lower than the cutoff value, the diagnosis result is negative, and the person is a healthy person;
when the kit is used for identifying gastric cancer and precancerous lesion, 0.732 is used as a cutoff value, and if the calculated comprehensive diagnosis index is higher than the cutoff value, the diagnosis result is gastric cancer; if the obtained comprehensive diagnosis index is lower than the cutoff value, the diagnosis result is gastric precancerous lesion.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (10)
1. A marker combination for use in the identification of gastric and gastric precancerous lesions or for the diagnosis of gastric cancer, said marker combination consisting of RP11-556o9.4, RP11-417e7.1, RPs3AP23, RP11-559m23.1, CTD-2339l15.3, DGCR9, LINC00567 and hsa _ circ _0047880.
2. Use of a reagent for detecting the expression level of the marker combination according to claim 1 in the preparation of a product for gastric cancer diagnosis.
3. The use of the reagent for detecting the expression level of the marker combination according to claim 1 in the preparation of products for identifying gastric cancer and precancerous lesion.
4. A kit for discriminating between gastric precancerous lesions and gastric cancer and diagnosing gastric cancer, comprising a reagent for detecting the expression level of the combination of the markers according to claim 1.
5. The kit according to claim 4, wherein the kit comprises a fluorescent quantitative PCR primer for detecting the expression level of the marker combination according to claim 1.
6. The kit according to claim 5, wherein the sequence of the fluorescent quantitative PCR primer for detecting RP11-556O9.4 is shown as SEQ ID NO. 9-10; the sequence of the fluorescent quantitative PCR primer for detecting RP11-417E7.1 is shown in SEQ ID NO. 11-12; the sequence of the fluorescent quantitative PCR primer for detecting RPS3AP23 is shown in SEQ ID NO. 13-14; the sequence of the fluorescent quantitative PCR primer for detecting RP11-559M23.1 is shown as SEQ ID NO. 15-16; the sequence of the fluorescent quantitative PCR primer for detecting CTD-2339L15.3 is shown as SEQ ID NO. 17-18; the sequence of the fluorescent quantitative PCR primer for detecting DGCR9 is shown as SEQ ID NO. 19-20; the sequence of the fluorescent quantitative PCR primer for detecting the LINC00567 is shown in SEQ ID NO. 21-22; the sequence of the fluorescent quantitative PCR primer for detecting hsa _ circ _0047880 is shown in SEQ ID NO. 23-24.
7. A comprehensive diagnostic model for discriminating gastric precancerous lesions and gastric cancer and diagnosing gastric cancer, which is characterized by using the marker combination of claim 1 as a target marker.
8. The integrated diagnostic model of claim 7, wherein the model calculates an integrated diagnostic index, cd-score, using the equation:
cd-score = -2.31858+0.11316 ha _circ _47880expression amount +3.15386 dgcr9 expression amount +1.48516 linc00567 expression amount +5.13114 ctd-233l15.3 expression amount-1.25785 rp11.417e7.1 expression amount-0.47492 rp11.556o9.4 expression amount +1.22980 rp11.5523.1 expression amount-0.09542 rps3ap23 expression amount.
9. The comprehensive diagnostic model of claim 8, wherein when the model is used for diagnosing gastric cancer, 0.331 is used as a cutoff value, and if the calculated comprehensive diagnostic index is higher than the cutoff value, the diagnosis result is positive, i.e. gastric cancer; if the obtained comprehensive diagnosis index is lower than the cutoff value, the diagnosis result is negative, and the person is a healthy person;
when the model is used for identifying gastric cancer and precancerous lesion, 0.732 is used as a cutoff value, and if the calculated comprehensive diagnosis index is higher than the cutoff value, the diagnosis result is gastric cancer; if the obtained comprehensive diagnosis index is lower than the cut-off value, the diagnosis result is gastric precancerous lesion.
10. A comprehensive diagnosis system for identifying gastric precancerous lesions and gastric cancer and diagnosing gastric cancer is characterized by comprising the following modules:
(1) A module for quantitatively detecting the expression amounts of RP11-556O9.4, RP11-417E7.1, RPS3AP23, RP11-559M23.1, CTD-2339L15.3, DGCR9, LINC00567 and hsa _ circ _0047880 in a sample respectively;
(2) A comprehensive diagnostic index calculation module: cd-score = -2.31858+0.11316 ha _circ _47880expression amount +3.15386 dgcr9 expression amount +1.48516 linc00567 expression amount +5.13114 ctd-233l15.3 expression amount-1.25785 rp11.417e7.1 expression amount-0.47492 rp11.556o9.4 expression amount +1.22980 rp11.5523.1 expression amount-0.09542 rps3ap23 expression amount;
(3) And a result judgment module: when the kit is used for diagnosing the gastric cancer, 0.331 is used as a cutoff value, and if the calculated comprehensive diagnosis index is higher than the cutoff value, the diagnosis result is positive, namely the gastric cancer is obtained; if the obtained comprehensive diagnosis index is lower than the cutoff value, the diagnosis result is negative, and the person is a healthy person;
when the kit is used for identifying gastric cancer and precancerous lesion, 0.732 is used as a cutoff value, and if the calculated comprehensive diagnosis index is higher than the cutoff value, the diagnosis result is gastric cancer; if the obtained comprehensive diagnosis index is lower than the cut-off value, the diagnosis result is gastric precancerous lesion.
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